(1) Field of Invention
The present invention relates to a robotic control system and, more particularly, to a system for controlling robotic prosthetic devices given motor intent inferred from neuroimaging data.
(2) Description of Related Art
Brain machine interfaces (BMI) and neural prosthetics offer great hope for restoring function to people with spinal cord injuries and amputees, as well augmenting and enhancing the abilities of people with full motor function. There have been a number of advances in neural imaging as well as in decoding motor intent from neuroimaging data. However, with regard to prosthetic device control there have been fewer advances, with most approaches rooted in conventional robotic control.
There is a body of work in BMI and neural prosthetics research which addresses the decoding of cortical signals for the downstream execution of motion commands by an external device (see, for example, the List of Incorporated Literature References, Literature Reference Nos. 1, 9, 11, 12, and 15). Most of this work focuses on the neural decoding with less rigor applied to the prosthesis control. In cases where a robotic arm prosthesis is controlled, the motion trajectory is typically converted into joint commands and executed using joint space control (see, for example, Literature Reference Nos. 11, 12, and 15).
To date, no known prosthetic control system has been proposed which uses a task/posture decomposition. Task/posture decomposition has particular advantages over joint space control in BMIs. It is based on an abstraction analogous to the manner in which cortical signals are encoded (e.g., eye-centered, hand-centered Cartesian coordinates). Further, it is generalizable to whole body prosthetic control involving multiple prosthetic limbs and high degree-of-freedom kinematics. Joint space prosthetic control is limited in this capacity due to its reliance on inverse kinematics solutions. Additionally, task/posture decomposition allows for the specification of postural behaviors based on minimizing important objective functions (e.g. power consumption, virtual muscle effort, etc.) consistent with the execution of cortical motion commands.
There has been some work on applying neuromorphic computing architectures to BMIs and neural prosthetic systems. For example, the work of Dethier et al. focused on the use of artificial spiking neural networks for decoding and filtering cortical signals (Kalman filter based decoding using endpoint kinematics as the state vector and neural spike rates as the measurement vector) rather than prosthetic control (see Literature Reference No. 7).
Additionally, the work of Bouganis and Shanahan was directed to controlling a robotic arm using artificial spiking neural networks; although their work was not applied to a neural prosthetic system (see Literature Reference No. 2).
Other researchers performed work in the simulation of neural prosthetic limbs in conjunction with biomechanical models (see Literature Reference Nos. 4, 5, and 8). However, while their work implements musculoskeletal models into the simulation, the focus of these efforts is on simulating and validating the control of the prosthetic device. Thus, again, there is no known system that integrates a sensorimotor controller, in addition to the prosthetic controller.
Therefore, a continuing need exists for a system that allows enhanced modeling and analysis of the interface issues associated, not just with the biological and engineered components, but also with the biological and engineered control systems. Further, a need exists for a system-level architecture for applying artificial spiking neural networks to the learning of motor maps, consistent with postural criteria, for controlling motion in BMI and neural prosthetic systems.